The success of a software development project depends on various factors, including timely delivery, meeting the needs of the customer, supporting the customer and providing a product of acceptable quality. Software development organizations collect a broad range of data and metrics during development, such as change requests, defect information, status of test cases, and others, to try and measure the status of the project in some fashion. Over the years, there have been a number of research efforts addressing the use of software metrics for in-process project management. Generally, this body of work focuses on specific sets of metrics to monitor various aspects of risk during the execution of a project. The metrics are evaluated one at a time using methods that may vary from individual to individual, and the overall risk of the project is evaluated subjectively from the individual metric evaluations. There is currently no systematic method to analyze software development data in an automated fashion to provide meaningful feedback about the software development process.
Therefore, a need exists to overcome the problems with the prior art as discussed above, and particularly for a way to simplify the task of providing automated risk assessments during a software development project.